Papers by Xiangdong Su
A Mutual Information Perspective on Knowledge Graph Embedding (2025.acl-long)
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| Challenge: | Existing knowledge graph embedding techniques suffer from high intra-group similarity, loss of semantic information, and insufficient inference capability, particularly in complex relation patterns such as 1-N and N-1 relations. |
| Approach: | They propose a knowledge graph embedding framework that leverages mutual information maximization to improve the semantic representation of entities and relations. |
| Outcome: | Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method, with consistent performance improvements across various baseline models. |
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning (2024.lrec-main)
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| Challenge: | Existing methods to generate reasoning programs that ignore the differences between facts treated all facts equally, leading to wrong punishment of programs that differed from the ground truth. |
| Approach: | They propose an optimized training framework for long-form numerical reasoning that incorporates a number-aware negative sampling strategy and consistency-based reinforcement learning to increase execution accuracy. |
| Outcome: | The proposed method improves the performance of long-form numerical reasoning on the FinQA and ConvFinQA leaderboards. |
TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation (2024.lrec-main)
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| Challenge: | Existing knowledge graph embedding models lack links between entities and relationships, which is a problem for knowledge graphs. |
| Approach: | They propose a translation-based knowledge geraph embedding method via efficient relation rotation that rotates the head and tail entities with their corresponding unit quaternions. |
| Outcome: | The proposed method can be used to embed knowledge graphs on 10 benchmark datasets with fewer parameters than the previous translation-based models. |
Know Your Place: Diagnosing Implicit Social Adaptation Failures in Chinese Large Language Models (2026.acl-long)
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| Challenge: | Existing studies suggest that failures of large language models in social contexts are not due to limited linguistic competence, but to inappropriate recognition. |
| Approach: | They propose a framework that decomposes social adaptation into three orthogonal dimensions and conduct controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions. |
| Outcome: | The proposed framework decomposes social adaptation into three orthogonal dimensions and conducts controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions. |
CausalityCheck: A Framework for Evaluating Causal Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing evaluation methods fail to accurately reflect a model's causal reasoning capabilities. |
| Approach: | They propose a tool to automatically generate causal reasoning checklists to assess the causal reasoning abilities of 18 large language models. |
| Outcome: | The proposed tool assesses the causal reasoning abilities of 18 large language models. |
Learning Low-dimensional Multi-domain Knowledge Graph Embedding via Dual Archimedean Spirals (2024.findings-acl)
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| Challenge: | Existing knowledge graph embedding methods make domain constraints on embeddable domains, leading to poor performance. |
| Approach: | They propose a low-dimensional KGE model for multi-domain knowledge graphs that embeds domains and domains by regularization function. |
| Outcome: | The proposed model can distinguish entities from domains by encoding the same relation on the same archimedean spiral. |
McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models (2025.findings-acl)
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| Challenge: | Existing datasets on bias evaluation for large language models focus on English and North American culture and are limited to one task. |
| Approach: | They propose to evaluate Chinese language models' biases from multiple perspectives using a multi-task Chinese Bias Evaluation Benchmark. |
| Outcome: | The proposed model covers 12, 82 subcategories and 5 evaluation tasks covering a wide range of categories and content diversity. |
F²Bench: An Open-ended Fairness Evaluation Benchmark for LLMs with Factuality Considerations (2025.emnlp-main)
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| Challenge: | Existing fairness evaluation benchmarks for large language models rely on closed-ended evaluation formats that overlook factuality considerations rooted in historical, social, physiological, and cultural contexts. |
| Approach: | They propose an open-ended fairness evaluation benchmark for large language models . they incorporate factuality considerations and multi-turn reasoning into the benchmark . |
| Outcome: | The proposed benchmark incorporates factual grounding and text generation to better reflect the complexities of real-world model usage. |
Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)
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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao
| Challenge: | Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding. |
| Approach: | They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language. |
EpLSA: Synergy of Expert-prefix Mixtures and Task-Oriented Latent Space Adaptation for Diverse Generative Reasoning (2024.lrec-main)
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| Challenge: | Existing models for diverse generative reasoning struggle to generate multiple unique and plausible results. |
| Approach: | They propose a model based on expert-prefix mixtures and task-oriented latent space adaptation for diverse generative reasoning. |
| Outcome: | The proposed model outperforms existing models on three types of generative reasoning tasks. |
TeAST: Temporal Knowledge Graph Embedding via Archimedean Spiral Timeline (2023.acl-long)
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| Challenge: | Existing temporal knowledge graph embedding models fuse temporal information into entities, limiting their effectiveness and potential applications. |
| Approach: | They propose a temporal knowledge graph embedding model which encodes Temporal knowledge graphs via Archimedean Spiral Timeline. |
| Outcome: | The proposed model outperforms existing TKGE methods in terms of relational consistency and interpretability. |
Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding (2025.naacl-long)
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| Challenge: | Existing studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding task. |
| Approach: | They propose a method that maps factor tensors onto a unified smooth Lie group manifold to approximate homogeneous in tensian decomposition. |
| Outcome: | The proposed method can be directly integrated into existing tensor decomposition based TKGE methods without introducing extra parameters. |
Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation (2020.coling-main)
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| Challenge: | Mongolian morphological segmentation is a crucial preprocessing step in many Mongolian related NLP applications. |
| Approach: | They propose a neural network incorporating inner-word and out-word features for Mongolian morphological segmentation. |
| Outcome: | The proposed network is compared with baselines and evaluates its performance. |
Exploring the Synergy of Dual-path Encoder and Alignment Module for Better Graph-to-Text Generation (2024.lrec-main)
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| Challenge: | KG-to-text generation model lacks explicit graph-text alignment strategy due to discrepancy between textual and structure information. |
| Approach: | They propose a synergetic knowledge graph-to-text model with a dual-path encoder, alignment module and guidance module to solve these problems. |
| Outcome: | The proposed model achieves competitive performance on three benchmark datasets. |
CEDAR: A Chinese Evaluation Dataset for Computational Argumentation (2026.acl-long)
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| Challenge: | Existing debate datasets neglect important labels for argument mining, generation, and evaluation. |
| Approach: | They propose a Chinese Evaluation Dataset for Computational Argumentation that includes key arguments and key rhetorical figures, debater roles, modal words, debate results and transcripts. |
| Outcome: | The proposed dataset covers 600 debates about 318 topics from Chinese debate competitions. |
How Well Apply Simple MLP to Incomplete Utterance Rewriting? (2023.acl-short)
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| Challenge: | Incomplete utterance rewriting (IUR) aims to restore incomplete utterant with sufficient context information for comprehension. |
| Approach: | They propose a method to restore incomplete utterances with sufficient context information . they employ only one-layer MLP architecture to mine latent semantic information based on joint utterations . |
| Outcome: | The proposed method is superior to existing methods in quality and speed. |
Hyperbolic Representations for Prompt Learning (2024.lrec-main)
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| Challenge: | Existing techniques to train only continuous prompts while freezing the language model have been developed. |
| Approach: | They propose to use hyperbolic space to model hierarchical relationships between prompts and inputs . they use a Poincaré disk to capture the hierarchic relationship between prompt and input . |
| Outcome: | The proposed approach reduces training time and storage for downstream tasks by reducing training costs. |
Born Pragmatic, Trained to Hallucinate? Quantifying the Origins of Contextual Bias in LLMs via the PaCE Benchmark (2026.findings-acl)
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| Challenge: | Large language models excel at capturing communicative intent, but they have a side effect: pragmatic hallucination. |
| Approach: | They propose a benchmark to quantify the impact of pragmatic hallucination on large language models . they propose RLHF and SFT to induce a strong tendency for pragmatic over-attribution . |
| Outcome: | The proposed model outperforms existing models in predicting pragmatic hallucinations . the evaluations show that current alignment paradigms lack precise control over pragmatic boundaries . |
Learning Continuous Temporal Dynamics on Symplectic Manifolds for Temporal Knowledge Graph Embedding (2026.findings-acl)
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| Challenge: | Existing methods for temporal knowledge graph embedding lack explicit structural constraints for continuous-time dynamics. |
| Approach: | They propose a Temporal Knowledge Graph Embedding framework that embeds temporal dynamics into a symplectic phase space. |
| Outcome: | The proposed framework achieves competitive performance with lower embedding dimensions. |
Lightweight Haar Wavelet Subband Pruning for LLMs (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive performance but require computational and memory resources. |
| Approach: | They propose a post-training framework that uses a Haar wavelet transform to prune weights. |
| Outcome: | The proposed pruning framework reduces pruning time and computational costs by removing less important weights while preserving model architecture. |
C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Sentence Ordering (2025.coling-main)
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| Challenge: | Existing sentence ordering datasets for non-English languages are unavailable. |
| Approach: | They propose a parameter-free sentence ordering dataset that provides genuinely unordered sentences without artificial segmentation cues. |
| Outcome: | The proposed method outperforms existing methods on the sentence ordering task. |
ToMELP: A Theory-of-Mind Benchmark for Route-Controlled Persuasion under the Elaboration Likelihood Model (2026.findings-acl)
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| Challenge: | Theory of Mind (ToM) is widely regarded as central to effective persuasion, yet existing evaluations fail to capture the infer–apply loop that arises in real-world dialogue. |
| Approach: | They propose a benchmark that conditions on the audience persona p and the Elaboration Likelihood Model (ELM) route r within persuasive conversations. |
| Outcome: | The proposed model can model the interlocutor's mental states over multiple turns and adapt strategy and tone accordingly. |
Leveraging 3D Gaussian for Temporal Knowledge Graph Embedding (2025.findings-emnlp)
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| Challenge: | Representation learning in knowledge graphs (KGs) has focused on static data, yet many real-world knowledge graph are inherently dynamic. |
| Approach: | They propose a temporal embedding method inspired by 3D Gaussian Splatting where entities, relations, and timestamps are modeled as 3D gaussian distributions with learnable structured covariance. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three benchmark TKG datasets. |
Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry (2026.acl-long)
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Jiang Li, Tian Lan, Shanshan Wang, Dongxing Zhang, Dianqing Lin, Guanglai Gao, Derek F. Wong, Xiangdong Su
| Challenge: | a recent study shows that large language models can generate text, but they can also fabricate large amounts of false or misleading content. |
| Approach: | They propose a benchmark to detect LLM-generated classical Chinese poetry . they compare 12 different AI detectors to find out whether a poem is authored by AI . |
| Outcome: | The proposed benchmark compared 12 AI detectors with a dataset of 30,664 Chinese poems . the results highlight the limitations of current Chinese text detectors . |
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models (2024.naacl-long)
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Yi Luo, Zhenghao Lin, YuHao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong
| Challenge: | Guide-Align is a guideline-oriented approach to augment the safety and quality of Large Language Models. |
| Approach: | They propose a guideline-oriented method to augment the safety and quality of large language models. |
| Outcome: | The proposed method outperforms existing methods on three benchmarks and shows significant improvements in security and quality. |